High efficiency water heater

ABSTRACT

A high-efficiency water heating system includes at least one source of heat and a processor interfaced to the at least one source of heat. The processor controls the operation of the at least one source of heat (e.g. energizes a heating element to provide heat to the water). At least one source of data related to a consumption of hot water from the high-efficiency water heating system is provided. Software running on the processor analyzes the data and calculates a predicted demand for the hot water based upon the data, then controls the operation of the at least one source of heat in response to the predicted demand.

FIELD

This invention relates to the field of providing hot water and moreparticularly to a system, apparatus, and method for predictively heatinghot water.

BACKGROUND

It has been estimated that water heaters account for up to 30 percent ofan average homes energy budget. Currently, there are two mainclassifications of home and industrial water heaters. The first mainclassification of water heater is known as a “convention storage tank”water heater. This type of water heater is commonly used in many homesand businesses and utilizes a source of heat and a storage tank. Heatfrom a source such as electrical heating elements or fossil fuel burnersincreases the temperature of water in the storage tank until it reachesa pre-determined temperature, at which time the heat source is shut off(conserving energy) until the temperature of the water in the storagetank drops to a second pre-determined temperature. Such systems providefor large, transient demands for hot water by providing large storagetanks or heating the water in the storage tanks to a high temperature,then mixing the hot water with unheated water before distributing thewater to the end users. Although the storage tanks are typicallythermally insulated, these systems lose efficiency due to heat loss dueto conduction from the storage tanks to the ambient environment andthrough the plumbing that connects the storage tanks to the water supplyand delivery plumbing, especially in situations where there are extendedperiods of time during which no hot water is used. Such situationsoccur, for example, in a residence when household members are at work orsleeping, or in an industrial facility when all of the workers are home.

The second main classification of water heater is known as an“on-demand” water heater. This type of water heater has no storage tank,instead having a very high energy output heating element that is capableof raising the temperature of the water from ground water temperature tothe desired hot water temperature as the hot water is used. Typically,the on-demand water heater has a flow sensor and a heating element suchthat, as soon as demand for hot water occurs, the sensor detects thatwater is flowing through the hot water plumbing and enables the heatingelement, which heats the water as the water flows through the heatingelement. This class of water heaters is more efficient than storage-tankbased water heaters because there is little or no energy loss when nohot water is being used. This class of water heaters has its own set ofdrawbacks. These water heaters require a large amount of energy duringoperation to enable the quick heating of water from ground watertemperatures to the desired hot water temperature at a given flow rate.As a result of this, such on-demand water heaters are often rated for acertain rise in temperature for a given flow rate. For example, oneon-demand water heater is capable of a 45 F rise in temperature at aflow rate of 8.5 gallons per minute, but only a 35 F rise in temperatureat a 9.5 gallon per minute rate. Therefore, if several people areconcurrently taking a shower, the hot water will not be as hot.Furthermore, because there is no storage tank containing pre-heatedwater, if there is a power failure, there will be no hot water untilpower is restored. Another setback of on-demand water heaters issupplying sufficient power for the heating elements. Natural gas isoften used because it provides a high amount of BTUs, but not all homeshave natural gas. Electricity is more widely available, but mostexisting buildings do not have sufficient power and/or wiring forwhole-house on-demand water heaters.

The storage-tank based hot water system has many advantages such assupplying sufficient hot water for most anticipated demands, constanthot water temperatures, providing a fair amount of hot water during apower outage, etc. Therefore, storage-tank based hot water systems willcontinue to be used.

Prior attempts at making improvements involved simple timers that weremanually set to prevent water heating during periods of no demand (e.g.when families are sleeping), systems that understand variable rate plans(e.g. electricity costs more during peak periods) to adjust heatingpatterns to the electricity costs, and systems that are remotelycontrolled by the electric companies to reduce electricity consumptionduring high demand periods, therefore preventing a grid overload.

What is needed is a system that will improve the overall efficiency ofthe storage-tank based hot water heater systems by predicting futureusage patterns.

SUMMARY

In one embodiment, a high-efficiency water heating system is disclosedincluding at least one source of heat and a processor interfaced to theat least one source of heat, The processor controls the operation of theat least one source of heat (e.g. energizes the at least one heatingelement to provide heat to the water). At least one source of datarelated to a consumption of hot water from the high-efficiency waterheating system is provided and software running on the processoranalyzes the data and calculates a predicted demand for the hot waterbased upon the data, then controls the operation of the at least onesource of heat responsive to the predicted demand.

In another embodiment, a method of providing hot water is disclosedincluding (a) calculating a predicted demand for hot water over a periodof time, then, (b) prior to and during the period of time, energizingone or more sources of heat based upon the predicted demand, the sourcesof heat interfaced to a water supply such that the source of heatprovide heat to water from the water supply and produce a quantity ofthe heated water. (c) A rate of flow of the heated water during theperiod of time is measured; and (d) the rate of flow of the heated wateris fed back into the step of calculating to improve the accuracy of thestep of calculating. Steps a-e are repeated.

In another embodiment, a high-efficiency water heating system isdisclosed including at least one source of heat operatively coupled to awater storage tank. When the at least one source of heat is energized,heat is transferred into water within the storage tank. At least oneon-demand heating device (e.g. gas burner or electric element) isconnected to a supply of water such that, when the at least oneon-demand heating element is energized, heat is transferred into waterfrom the supply of water by the at least one on-demand heating device.Two valves control the flow of water: a first electrically controlledvalve connecting the output of the storage tank to supply the heatedwater and a second electrically controlled valve connecting the outputof the at least one on-demand heating device to supply the heated water.A processor is interfaced to the at least one source of heat, to the atleast one on-demand heating device, to the first valve, and to thesecond valve, the processor controlling the operation of the at leastone source of heat, the at least one on-demand heating device, the firstvalve, and the second valve. A flow sensor is operatively coupled to theprocessor, providing a measurement of current demand for the heatedwater. At least one source of data related to a consumption of the hotwater from the high-efficiency water heating system is provided.Software running on the processor analyzes the data and calculates apredicted demand for the hot water and controls the operation of the atleast one source of heat, the at least one on-demand heating device, thefirst valve, and the second valve, responsive to the predicted demand.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be best understood by those having ordinary skill inthe art by reference to the following detailed description whenconsidered in conjunction with the accompanying drawings in which:

FIG. 1 illustrates a schematic view of a high-efficiency hot waterheater software system.

FIG. 2 illustrates a schematic view of the high-efficiency hot waterheater decision process.

FIG. 3 illustrates a schematic view of a typical computer system of thehigh-efficiency hot water heater.

FIG. 4A illustrates a schematic view of a typical storage-tank hot waterheater of the prior art.

FIG. 4B illustrates a schematic view of a typical on-demand hot waterheater of the prior art.

FIG. 5 illustrates a simplified schematic view of the high-efficiencyhot water heater.

FIG. 6 illustrates a second schematic view of the high-efficiency hotwater heater.

FIG. 7 illustrates a flow chart of a storage-tank hot water heater ofthe prior art.

FIG. 8 illustrates a flow chart of an on-demand hot water heater of theprior art.

FIG. 9 illustrates a flow chart of a high-efficiency hot water heater.

FIG. 10 illustrates a schematic diagram of a neural-network-basedhigh-efficiency hot water heater.

DETAILED DESCRIPTION

Reference will now be made in detail to the presently preferredembodiments of the invention, examples of which are illustrated in theaccompanying drawings. Throughout the following detailed description,the same reference numerals refer to the same elements in all figures.

Throughout this description, the term “heating element” refers to anytype of heating element, including, but not limited to, electric heatingelements, gas burners, oil burners, etc. The heating element(s), whenenergized, provide heat to a body of water. Note that the colloquialmeaning of water includes H₂0 and any impurities present in a watersupply such as other chemicals, minerals, solids, and dissolved gasses.

Throughout the description, the terms “hot water” and “heated water”refers to water that has been heated above the ambient water temperatureas one would expect to find when opening a left spigot on a sink or onethat is marked ‘H’ in English speaking countries like the United States(note, other marks exist for other countries such as ‘C’ for Caldo inItaly).

Throughout the description, embodiments are shown having a certainnumber and type/location of storage tanks, heating elements and valves.The high-efficiency hot water heater is not restricted in any way to anyparticular number of storage tanks, heating elements or heat sources,and/or valves. For example, in some embodiments, there are no valves andin some embodiments, there is only one “mixing” valve. Thehigh-efficiency hot water heater is not restricted in any way to anyspecific source of heat configuration/combination. For example, somestorage tank systems have upper and lower heating elements while othershave only a single gas burner, while still other systems have heatsources (e.g. boilers, solar panels), that are external to the storagetanks. There are no restrictions as to the types of heat sources and/orlocations of the heat sources.

Although described as a processor-based system, as known, anyprocessor-based system is capable of being made using discretecomponents (e.g. gates) and such implementations are anticipated andincluded here within.

Any form of control of the valves and heating elements is anticipated,including, but not limited to, electrical control through individualwiring, electrical control through a wired network, electrical controlthrough a wireless system, pneumatic control, etc.

Throughout this description, various examples of home and/or industrialwater heating scenarios are provided. The disclosed and claimedinvention is not limited in any way to a particular application and isintended for use in any water heating application.

Referring to FIG. 1, a schematic view of a high-efficiency hot waterheater software system is shown. The system shown in FIG. 1 applies tosome embodiments of the high-efficiency hot water heater softwaresystem. Although the high-efficiency hot water heater software system isintended to operate with or without a network 10, it is shown interfacedto the Internet 10 (a.k.a. the World Wide Web).

In this embodiment, one or more heater controllers 20 are connected tothe network 10 as known in the industry. One or more servers 40 are alsoconnected to the network 10 as known in the industry. Thehigh-efficiency hot water heater software system includes data 42 forauthentication as well as history, etc. It is anticipated that any orall of the storage areas 42 are locally interfaced to the server 40,remotely interfaced to the server 40 (e.g., Network AttachedStorage—NAS) and/or remotely interfaced to the server 40 over a network,either a local area network or wide area network.

In some embodiments, the server 40 or the individual heater controllers20 also interfaces to various providers of external data 50. Theinformation providers 50 are interfaced to the server 40 and/or heatercontrollers 20 with any known network or direct connection, as known inthe industry. As shown in the example of FIG. 1, the informationproviders 50 are interfaced to the server 40 through the Internet 10.When used, the information providers 50 deliver external data to theserver 40 and/or heater controllers 20 related to hot water demand suchas weather predictions, sunrise and sunset times, etc. Thehigh-efficiency hot water heater uses this data, when available, to helppredict future hot water demand. For example, if it will be cloudy, morehot water is likely to be needed because people taking showers feelcolder on cloudy days.

The architecture shown in FIG. 1 is flexible, in that, the decisionprocess is made either locally at the hot water heater controllers 20(see FIGS. 3, 5 and 6) or centrally at a central computing system(server) 40 or jointly. Therefore, several high-efficiency hot waterheater system architectures/configurations are anticipated. In somehigh-efficiency hot water heater systems, a controller 20 (see FIG. 3)is directly interfaced to the heating elements 120/122 (see FIGS. 5 and6), obtaining data locally and/or remotely (e.g. from InformationProviders 50) and making all decisions on when and how to heat water. Insome high-efficiency hot water heater systems, the controller 20 orother discrete logic is interfaced to the heating elements 120/122 (seeFIGS. 5 and 6) and is controlled by software running on a remote system(e.g. server 40). In this, the remote system (server 40) optionallyreceives data from the local controller 20 and/or remotely (e.g. fromInformation Providers 50) and makes all decisions on when and how toheat water, instructing the controller 20 to turn on/off the heatingelements 120/122. In another type of high-efficiency hot water heatersystem, a combination of the prior architectures is used. In this, thecontroller 20 (see FIG. 3) or other discrete logic is directlyinterfaced to the heating elements 120/122 (see FIGS. 5 and 6) and makespart of the decisions as to when and how to heat the water, but theprocessor 70 is also in communication and cooperating with softwarerunning on a remote system (e.g. server 40). In this, the remote system(server 40) optionally receives data from the local controller 20 and/orremotely (e.g. from Information Providers 50) and jointly, with thecontroller 20, makes decisions on when and how to heat water.

Referring to FIG. 2, a schematic overview of the high-efficiency hotwater heater decision process is shown. In this example, one or moreinputs 32/33/34/35/36 are analyzed by analysis algorithms 30 todetermine how to control the high-efficiency hot water heater heatingelements 120/122 and or valves 130/132/134 (see FIGS. 5 and 6). Any orall of the inputs 32/33/34/35/36 are used to predict when differentamounts of hot water will be needed. For example, ambient conditions 32are used by the analysis algorithms 30 to predict hot water needs. Forexample, a person will adjust the water temperature of their showerdependent upon internal building ambient temperature and humidity. Ahigher internal building ambient temperature and humidity tends to makea person lower their shower temperature while a lower internal buildingambient temperature and humidity tends to make a person increase theirshower temperature. Likewise, external ambient conditions tend tofurther influence a person's shower temperature. For example, when it iscloudy out, a person tends to take warmer showers and when it is hotoutside, after exercise outdoors, a person tends to take cooler showers.Ambient conditions 32 are measured by, for example, temperature sensors100/104, humidity sensors 110, and light sensors 112 (see FIG. 3).

Schedule data 33 is also optionally considered by the analysisalgorithms 30 to predict when demand will occur. For example, in adormitory, by knowing the schedule of students, the analysis algorithms30 control the heater(s) 120/122 and valves 130/132/134 based upon theschedule data 33 such that, knowing that lights out starts at 10:00 PMand classes start at 8:00 AM, the analysis algorithms 30 predict verylow hot water usage after 10:00 PM when the students are asleep, highhot water usage prior to 8:00 AM when students are waking and takingshowers, and low hot water usage when students are in class after 8:00AM, etc. In another example, by knowing the schedule data 33 for peoplein a home, the analysis algorithms 30 make similar predictions. Forexample, in a two person household, knowing that both occupants leavework at 7:30 AM and return home at 6:00 PM on Monday, Tuesday andFriday, the analysis algorithms 30 predict low or no hot water usagestarting at 7:30 AM and pre-heat the hot water in the storage tank 140(see FIG. 5) to reach a certain temperature at 6:00 PM when theoccupants arrive home (e.g. for dish washing, showers, etc.). In someexamples, schedule data includes resource levels such as: there are 40residents in the dormitory, twenty start classes at 8:00 AM and 40residents start classes at 9:15 AM. In this example, the number ofstudents is also used to determine hot water demand (e.g. if there weremore than 40 students, more hot water is needed).

Current data 35 is also (optionally) considered by the analysisalgorithms 30 to predict when demand will occur. Current data includes,for example, current flow rates, current hot water output temperature,and current system input water temperature. The analysis algorithms 30use the current data 35 to determine how well predictions match actualand if any boosting or setback is needed based upon this current data35. For example, if the analysis algorithms 30 predict an overall demandof 600 gallons of hot water per hour (10 gallons per minute) and thecurrent data 35 indicates that in the last 5 minutes, 30 gallons perminute have been consumed, then the analysis algorithms will control thewater heating elements 120/122 to energize and heat the watertemperature to a higher temperature than was predicted to be required.Likewise, if for some reason the temperature of the input water is a fewdegrees less than it has been; more heat needs to be added to the systemto account for colder water being mixed with the heated water in thestorage tank 140 (see FIGS. 5 and 6).

Another optional source of data to the analysis algorithms 30 isexternal data 36. External data includes any data feed that hasinformation regarding the future demand for hot water. This dataincludes, but is not limited to, weather predictions, data from analmanac (e.g. sunrise and sunset times), local news information, schoolinformation, lunch menus, school events, local events, etc. For example,if the dormitory dinner menu includes cold sandwiches, there is likelyto be a higher demand for hot water that evening then if the menuincludes hot soup. If the forecast for tomorrow is rain and sleet,depending on the users, such weather will change demand. For example,some hot water users will forego showering/bathing until they returnfrom classes so as to not be as cold walking to classes. Other eventswill affect hot water consumption. For example, if Friday night is PromNight, then extra hot water will be needed between, say, 5:00 PM and7:00 PM for prom goers to shower/bathe.

Although similar to schedule data 33, historic data 34 differs, in thatdemand measurements from the past are used to predict future demand bythe analysis algorithms 30. For example, after installation of thehigh-efficiency hot water heater system, the analysis algorithms 30initially relies on ambient data 32, schedule data 33, external data 36and current data 35 to predict hot water demand and control the hotwater heaters 60. After a number of days of controlling thehigh-efficiency hot water heater based upon these data, the analysisalgorithms now have access to historical data 34. For example, one setof 40 students may have hot water usage patterns that differ fromanother set of the same number of students, perhaps due to differencesin gender and other backgrounds. Initially, the analysis algorithms 30make an assumption of the hot water needs per person, for example,making an average consumption prediction or a worst-case consumptionprediction. As time goes on and usage patterns start repeating, theanalysis algorithms 30 consult the historic data 34 to determine how theoperation of the high-efficiency hot water heater needs to be adjustedbased upon the historic data 34. For example, the first Monday afterinstallation, the high-efficiency hot water heating system preheats thewater in storage to a certain temperature based upon 40 unknown people.By measuring the flow rates, input water temperature and output watertemperature, the analysis algorithms determine that the water in thestorage tank 140 does not need to be heated as much as it was, so thenext Monday, the water in the storage tank 140 is heated a percentageless (e.g. 10% less) and the flow rates, input water temperature andoutput water temperature are again measured to determine how well thehigh-efficiency hot water heating system is meeting the demands ofusers.

Although not required, it is anticipated that the analysis algorithms 30use neural networks 430 (see FIG. 10) or some other form of artificialintelligence to analyze the data 32/33/34/35/36 and make appropriatedecisions on when and how much to pre-heat the water based uponpredictions of such analysis algorithms. A neural network 430 acceptsdata (as described above) and determines actions (as described above),with the added benefit that the neural network 430 learns. The neuralnetworks 430 find patterns in the data as well as filter the data. Forexample, over time, the neural network 430 will find that every Fridaymorning between 6:00 and 6:15, there is a high demand for hot water and,therefore, will make assumptions on how the water heater needs to becontrolled based upon the predicted demand. The neural network 430 willfilter its data to ignore irregular data. For example, when users are onvacation and during one week, there is no demand for hot water on thatday.

In the neural network 430 implementation, each input is considered witha weighing factor. For example, last week's usage history has a highweighing factor, the week before usage history has a lower weighingfactor, and the external weather (e.g. cloudy, raining) has even a lowerweighing factor. As the neural network system 430 continues to predicthot water demand, hot water usage (e.g. flow rates) is measured and fedback into the neural network 430 and the neural network 430 makesadjustments. For example, if, over time, the neural network 430recognizes that hot water demand is 10% higher on cloudy days, theneural network 430 will increase the weight given to external weather.

Being that neural networks 430 are well known, it is anticipated thatfor some high-efficiency hot water heaters, the basic neural network 430software is provided as a package from a provider of such and isprogrammed based upon the range of inputs available to thehigh-efficiency hot water heater (e.g., schedule, ambient/weather,history, current data, external data, etc.) to control the availableheating elements 120/122 (see FIGS. 5 and 6) and/or valves 130/132/134(see FIGS. 6 and 7). For some high-efficiency hot water heater systems,the neural network software is not an off-the-shelf pre-programmedpackage.

In some high-efficiency hot water heaters, instead of using true neuralnetworks, heuristic algorithms or static logic is used in the predictionalgorithms 30. A simple example in a dormitory in which all studentsleave for breakfast and class at the same time and there are n studentsregistered for that dormitory, an exemplary heuristic algorithm is: if nis less than 30, preheat the water to t1 at time T1; if n is greaterthan 30 and less than 60, preheat the water to t2 at time T2; and if nis greater than 60, preheat the water to t3 at time T3. In this, theschedule data 33 is used to determine when the students will be usingthe hot water (e.g. before class/breakfast) and how many students arepresent. The more students present, the hotter the water in the storagetank needs to be, therefore, heating starts earlier and ends when thewater reaches this higher temperature. This is but an example and acomplete heuristic algorithm will consider other data in the algorithm'sdecision tree.

Referring to FIG. 3, a schematic view of a typical computer system isshown. The example computer system represents a typical computer systemused as the individual heater control devices 20, though a similarcomputer system is anticipated for the server 40. The exemplary computersystem 20 is shown in its simplest form, having a single processor 70.Many different computer architectures are known that accomplish similarresults in a similar fashion and the present invention is not limited inany way to any particular computer system. The present invention workswell utilizing a single processor system, as shown in FIG. 3, a multipleprocessor system where multiple processors share resources such asmemory and storage, a multiple server system where several independentservers operate in parallel (perhaps having shared access to the data orany combination). In any of these systems, a processor 70 executes orruns stored programs that are generally stored for execution within amemory 74. The processor 70 is any processor or a group of processors,for example an Intel Pentium-4® CPU, 80C51, or the like. The memory 74is connected to the processor by a memory bus 72 and is any memory 74suitable for connection with the selected processor 210, such as SRAM,DRAM, SDRAM, RDRAM, DDR, DDR-2, etc. It is also anticipated that theprocessor 70, bus 72 and memory 74 are integrated into a singlecomponent.

Also connected to the processor 70 is a system bus 82 for connecting toperipheral subsystems such as a network interface 80, persistent storage(e.g. a hard disk, flash memory) 88, removable storage (e.g. DVD, CD,flash drive) 90, a graphics adapter 84 and a keyboard/mouse 92. Thegraphics adapter 84 receives commands and display information from thesystem bus 82 and generates a display image that is displayed on thedisplay 86 (e.g. monitor, LEDs, graphic display, etc.).

Various input devices, sensors, and control drivers100/104/110/112/114/116/118 are optionally connected to the bus. Thefollowing inputs are representative of inputs for the high-efficiencyhot water system, though more or less inputs are anticipated: one ormore outside ambient temperature sensors (t_(a)) 100, one or more indoorbuilding temperature sensors (t_(b)) 104, one or more relative humiditysensors 110, one or more outdoor ambient light sensors (e.g. cloudcover) 112, cold water supply temperature sensor (t_(i)) 115, hot wateroutput temperature sensor (t_(o)) 117, and a water flow sensor 114.

The exemplary control outputs include one or more heater controls 118and one or more valve controls 116. The heater controls 118 energize oneor more water heating elements 120/122 (see FIGS. 5 and 6). The valvecontrols 116 energize (open or close or partially open) one or morewater control valves 130/132/134 (see FIGS. 5 and 6). Although shown asdiscrete inputs 100/104/110/112/114/115/117 and discrete outputs116/118, it is anticipated that in some systems, the sensors andcontrols 100/104/110/112/114/115/117/116/118 are signaled through a bus,wired and/or wireless, such as Inter-Integrated Circuit (I²C), car-areanetwork bus (CAN), vehicle area network bus (VAN), Ethernet, Bluetooth,IEEE 488, USB, FireWire (1394), RS232 bus, Wi-Fi, etc.

In general, the persistent storage 88 is used to store programs,executable code and data such as user financial data in a persistentmanner. The removable storage 90 is used to load/store programs,executable code, images and data onto the persistent storage 88. Theseperipherals are just examples of input/output devices 80/84/92,persistent storage 88 and removable storage 90. Other examples ofpersistent storage include core memory, FRAM, flash memory, etc. Otherexamples of removable media storage include CDRW, DVD, DVD writeable,Blu-ray, compact flash, other removable flash media, floppy disk, ZIP®,etc. In some embodiments, other devices are connected to the systemthrough the system bus 82 or with other input-outputconnections/arrangements as known in the industry. Examples of thesedevices include printers; graphics tablets; joysticks; andcommunications adapters such as modems and Ethernet adapters. Anyconfiguration of input/output devices is anticipated and thehigh-efficiency hot water heater system is not limited to any particulararchitecture and/or configuration.

In some high-efficiency hot water heater systems that communicate with acentral server 40 and/or external information providers 50, a networkinterface 80 connects the processor 70 to the network 10 through a link78 which is any known network media such as a cable broadbandconnection, a Digital Subscriber Loop (DSL) broadband connection, a T1line, a T3 line, or a wireless link such as Wi-Fi, or a cellular dataconnection.

Referring to FIG. 4A, a schematic view of a typical storage-tank hotwater heater of the prior art is shown. In this example of the priorart, cold water enters the tank 1 and is heated by a heating element 2that is controlled by a thermostat to attempt to maintain thetemperature of the water in the tank 1 to a certain temperature (e.g.125° F.). This example of the prior art always tries to maintain thatcertain temperature, even during the evening or when the coveredbuilding is vacant and there is no demand for hot water. Because it isconstantly maintaining that certain temperature, as heat is lost throughthe insulation that typically surrounds the tank 1 and through thesupply and output connections, energy is consumed by the heating element2, even though there is no actual use of hot water.

Referring to FIG. 4B, a schematic view of a typical on-demand hot waterheater of the prior art is shown. In this example of the prior art, coldwater is heated instantaneously by one or more heating elements 3 thatis/are controlled by a flow sensor such that, as soon as demand for hotwater occurs (e.g. a tap is opened), the heating elements 3 areenergized to heat water from the cold water supply to a desired outputtemperature. On-demand hot water heaters of the prior art are typicallymore efficient that storage-tank systems as shown in FIG. 4B, but due tothe maximum capabilities of heating elements 3 in raising thetemperature of the cold water supply, when multiple demands are placedon these on-demand hot water heaters, they often do not provide hotwater that is sufficiently hot. On-demand hot water heaters will delivercooler hot water when several people are filling bathtubs, showering,doing laundry and/or washing dishes. Additionally, because there is nohot water storage, on-demand hot water heaters will not supply hot waterduring a power failure.

Referring to FIG. 5, a simplified schematic view of the high-efficiencyhot water heater is shown. The exemplary high-efficiency hot waterheater has a hot water storage tank 140 with one or more sources of heatsuch as heating element(s) 122 (or main heater) and one or moreon-demand heaters 120. Any sources of heat are anticipated for theheating elements 122 associated with the tank 140 and for the on-demandheaters 120, including, but not limited to, electric heating elements,gas flames, oil flames, hot fluids from a boiler, hot fluids from solarpanels, etc.

Valves 130/132/134 control the flow of water such that the output (HW)is supplied either directly from the hot water tank (tank input valve130 is open, tank output valve 132 is open and tank bypass valve 134 isclosed) or directly from the on-demand heaters 120 (tank input valve 130is closed, tank output valve 132 is closed and tank bypass valve 134 isopen) or a combination of both through partial operation of the valves130/132/134.

Note that, in some embodiments, there are no internal heating elements122 and all water heating is performed, for example, by the one or moreon-demand heaters 120 or provided by other heat sources such as boilersand solar collectors.

In this example, cold water from the supply (CW) is heated by theon-demand heating elements 120 to feed the tank 140 while thetemperature within the tank 140 is maintained by an internal heatingelement 122. The valves 130/132/134 and heating elements 120/122 arecontrolled by the controller based upon the analysis algorithms 30. Forexample, in a dormitory situation, at 1:00 AM (low demand period), theinternal heating element 122 maintains a lower water temperature in thetank 140 and, should demand for hot water occur, one or more of theon-demand heating elements 120 is energized and hot water is routeddirectly from the on-demand heating elements 120 (tank input valve 130is closed, tank output valve 132 is closed and tank bypass valve 134 isopen). At a time later in the morning, based upon a prediction of theanalysis algorithms 30, the internal heating elements 122 are energizedto bring the water temperature in the storage tank 140 to a highertemperature to meet a high demand as students start to wake.

Note that in some embodiments, there are no on-demand heating elements120 and, therefore, no need for valves 130/132/134. In such a minimalsystem, the controller 20 predictively controls operation of theinternal heating element(s) 122, but such a system lacks on-demandheating elements 120, during periods where demand is not expected demandresults in lower temperature hot water.

Referring to FIG. 6, a second schematic view of the high-efficiency hotwater heater is shown. In this example, the controlled devices120/122/130/132/134 and sensors/inputs100S/104S/110S/112S/114S/115S/117S are connected to the controller 20through a bus 125 to simplify wiring. Alternatively, one or more of thecontrolled devices 120/122/130/132/134 and/or sensors/inputs100S/104S/110S/112S/114S/115S/117S are directly connected to the outputs116/118 and inputs 100/104/110/112/114/115/117 of the controller 20(e.g. temperature sensor 100S is directly connected to input 100). Inthis example, the controller 20 is also connected to the network 10(e.g. Internet) for communication with the server 40 and/or one or moreinformation providers 50.

This exemplary high-efficiency hot water heater has a hot water storagetank 140 with internal heating element(s) 122 (or main heater) and oneor more on-demand heaters 120. Valves 130/132/134 control the flow ofwater such that the output (HW) is supplied either directly from the hotwater tank (tank input valve 130 is open, tank output valve 132 is openand tank bypass valve 134 is closed) or directly from the on-demandheaters 120 (tank input valve 130 is closed, tank output valve 132 isclosed and tank bypass valve 134 is open) or a combination of boththrough partial operation of the valves 130/132/134. Operation of theheating elements 120/122 and valves 130/132/134 is controlled by theprocessor 70 of the controller 20 sending and receiving signals over thebus 125.

In this example, cold water from the supply (CW) is heated by theon-demand heating elements 120 to feed the tank 140 while thetemperature within the tank 140 is maintained or increased by heatingelement(s) 122 associated with the storage tank 140 (e.g. internalheating element(s) 122). The valves 130/132/134 and heating elements120/122 are controlled by the controller 20 based upon the analysisalgorithms 30 as previously described.

Note that in some embodiments, there are no on-demand heating elements120 and, therefore, no need for valves 130/132/134. In such a minimalsystem, the controller 20 predictively controls operation of the heatingelements 122 that provide heat to water within the storage tank 140, butsuch a system without on-demand heating elements 120 will not provideproperly heated water during periods where demand is not expectedbecause the water temperature in the storage tank 140 is typicallyallowed to decrease during such periods.

Referring to FIG. 7, a flow chart of a storage-tank hot water heater ofthe prior art is shown. In the prior art, the typical water heater (e.g.50 gallon home water heaters) has simple, thermostatically controlledheating elements 2 within the storage tank 1 (see FIG. 4A). In suchheaters, the heating element is initially de-energized 200, then thefollowing steps repeated (though this is typically a hardwareimplementation rather than a software implementation). The watertemperature within the storage tank 1 is measured 202 and compared to alow-threshold 204. If the water temperature within the storage tank 1 isbelow the low-threshold 204, the heating element is energized 206. Thewater temperature within the storage tank 1 is then compared to ahigh-threshold 208 and, if the water temperature within the storage tank1 is higher than the high-threshold 208, the heating element is disabled210. In this exemplary system of the prior art, the heating elementscontinue to maintain the water temperature within the storage tank 1 ata temperature between the low-threshold and the high-threshold, evenwhen the occupants of the building being served are not present (e.g. atwork, on vacation) or have a low probability of using hot water (e.g.asleep).

Referring to FIG. 8, a flow chart of an on-demand hot water heater ofthe prior art is shown. In the prior art, the typical on-demand waterheater has a simple, flow-detection controlled heating element(s) 3 (seeFIG. 4B). In such heaters, the heating element is initially de-energized220, then the following steps repeated (though this is typically ahardware implementation rather than a software implementation). The flowof water is measured 222 and compared to a threshold 224. If the flow ofwater is above the threshold 224, the heating element is energized 226.If the flow of water is below the threshold 224, the heating element isde-energized 228. In this exemplary system of the prior art, the heatingelements 3 must have sufficient power output to heat the water as itflows around the heating elements 3. The amount of heat needed to raisethe temperature from ground water temperature to the desired hot watertemperature is directly proportional to the flow rate and, therefore, ifthe flow rate exceeds the design limits of the heating element(s) 3, allusers will experience water that is not as hot as desired. Furthermore,because there is no storage tank 1 (as in FIG. 4A), if there is a powerfailure, this water heater of the prior art will produce no hot water.This is especially bad if there is a power failure in the morning beforeoccupants of the affected building leave for work, assuming that theiralarm clocks wake them during the power failure.

Referring to FIG. 9, a flow chart of an exemplary high-efficiency hotwater heater is shown. This flow chart is greatly simplified to providea basic understanding of the operation of the high-efficiency hot waterheater. This example uses heating elements (e.g. electric heatingelements) as the sources of heat for simplicity purposes. The heatingelements 120/122 are initially de-energized 300 then the following stepsrepeated: (a) The flow rate is measured 302, then (b) the flow rate iscompared to a first threshold 304 and if greater than the firstthreshold THR1 304 (e.g. there is at least some flow of water indicatingdemand for heated water), the flow rate is compared 306 to THR2, asecond, higher flow rate (i.e. is there low demand or high demand?). Ifthe flow rate is higher than the THR2 (high demand), then the on-demandheating element 120 is energized (H=1) and all valves are open (V1=1,V2=1, V3=1) 308, utilizing any already heated water from the storagetank 140 to provide the greatest amount of hot water as possible. If theflow rate is lower than the THR2 (low demand), then the on-demandheating elements 120 are energized (H=1), the valve between theon-demand heating elements 120 is opened (V3=1), and the valves leadingto and from the storage tank 140 are closed (V1=0, V2=0) 310 providingsufficient water heating for the intermediate demand because theon-demand heating elements 120 are sized to supply the intermediatedemand.

Next, the history file is consulted 322. The history file contains, forexample, historical usage patterns sorted by meaningful calendar periods(e.g. days of the week). For example, an exemplary history file for adormitory might show the following:

Day Hour Usage Monday Midnight-6:00 AM Low Monday 6:00 AM-6:45 AMModerate Monday 6:45 AM-8:00 AM High Monday 8:00 AM-5:45 PM Low Monday5:45 PM-11:59 PM Moderate

The above example only shows one day of the exemplary history file forbrevity purposes. As hot water is used, the high-efficiency hot waterheater updates the history file, with or without data smoothing toignore unusually high or low demands. In this example, there is littleor no need to heat the water in the storage tank 140 between the hoursof midnight and 6:00 AM, being that there is low demand and theon-demand heating elements 120 are capable of supplying any predicteddemand during that period. In such cases, the need predicted test 324results in not needed and the storage tank heater is set to off (I−H=0)326. At 6:00, it is predicted that moderate usage will start and lastfor 45 minutes. Again, the on-demand heating elements 120 are capable ofsupplying any predicted demand during that period, but the analysisalgorithms 30 recognize that starting at 6:45 AM, the demand will behigh (e.g. greater than THR2), so high that the on-demand heatingelements 120 will not be capable of supplying the predicted demand from6:45 AM to 8:00 AM. Therefore, the need predicted test 324 determines ahigh need (flow>THR2) and the storage tank heater is energized (I−H=1)328 to preheat the water in the storage tank 140 to meet the expectedhigh demand period that starts at 6:45 AM. At around 7:40 AM, the needpredicted test 324 determines that sufficient hot water is alreadyavailable within the storage tank 140, enough to supplement theon-demand heating elements 120. The storage tank heating element(s) 122is again de-energized 328 and the demand for the next 20 minutes or so(until 8:00 AM) is met by the hot water already in the storage tank 140and the on-demand heating elements 122. Because the remainder of theday, only low or moderate usage is predicted, there is no need toenergize the storage tank heating elements 122 being that the on-demandheating elements are capable of supplying all predicted demand for thatperiod.

The above example is for illustrative purposes and it is known that asystem would generally be much more complicated and have more or lessdata.

Referring to FIG. 10, a schematic diagram of a neural-network-basedhigh-efficiency hot water heater is shown. The data available as inputs(ambient 32, schedule data 33, external data 36, current data 35 andhistory 34) are examples of input data for the neural network 430 and itis anticipated that high-efficiency hot water systems will have more,less, and/or different inputs. In this example, one or more inputs32/33/34/35/36 are analyzed by a neural network 430, controlling theheating elements 120/122 (sources of heat). Any or all of the inputs32/33/34/35/36 are used by the neural network 430 to predict whendifferent amounts of hot water will be needed. For example, ambientconditions 3 (e.g. weather) are used by the neural network 430 topredict hot water needs based upon internal weighing factors and otherinputs. For example, if previously, users adjusted the water temperatureof their shower dependent upon outside temperature and humidity, theneural network will “learn” and add intelligence that uses the ambientdata 32 to predict future hot water demands. In another example,internal ambient conditions tend to influence a person's showertemperature. For example, when there is a low humidity in the building,a person tends to take warmer showers. The neural network 430 monitorssuch inputs and makes inferences based upon previous ambient data 32 topredict future hot water demands.

Schedule data 33 is also considered by the neural network 430 to predictwhen demand will occur. For example, in a dormitory, by knowing weighvalues for various aspects of the schedule of students along with theother data, the neural network 430 controls the on-demand heatingelements 120 and the heating elements 122 associated with the storagetank 140 based upon the data 32/33/34/35/36. With this data, the neuralnetwork 430 will learn that lights out starts at 10:00 PM and classesstart at 8:00 AM and, therefore, the neural network 430 will learn topredict very low hot water demand after 10:00 PM when the students areasleep, high hot water usage prior to 8:00 AM when students are wakingand taking showers, and low hot water usage when students are in classafter 8:00 AM, etc.

Current data 35 is also (optionally) considered by the neural network430 to predict when demand will occur. Current data includes, forexample, current flow rates, current hot water output temperature, andcurrent system input water temperature, etc. The neural network 430 alsouses the current data 35 to determine how well predictions match actualusage and if any boosting or setback is needed based upon the currentdata 35. For example, if low usage is predicted for the upcomingtimeframe but the current data 35 shows that high usage is occurring,then the neural network 430 reacts to such by energizing heatingelements 120/122 and controlling valves 130/132/134 to provide maximumhot water output.

Another optional source of data to the analysis algorithms 30 isexternal data 36. External data includes any data feed that has thepotential to effect hot water usage. This data includes, but is notlimited to, weather predictions, data from an almanac (e.g. sunrise andsunset times), local news information, school information, lunch menus,school events, local events, etc. When such external data 36 isavailable, the neural network 430 also considers such in makingpredictions.

Although similar to schedule data 33, historic data 34 differs, in that,demand measurements from the past are used by the neural network 430 topredict future demand. For example, after installation of thehigh-efficiency hot water heater system, the neural network 430 istaught to predict demand based upon on ambient data 32, schedule data33, external data 36 and current data 35, saving historical data anddata related to its own performance in the history data 34. After anumber of days of controlling the heating elements 120/122 and valves130/132/134 based upon these data 32/33/35/36, the neural network 430now has access to historical data 34. For example, one set of 40students may have different hot water usage patterns that differ fromanother set of the same number of students, perhaps due to differencesin gender and other backgrounds. Initially, the neural network 430 istaught to make an assumption of the hot water needs per person, forexample, making an average consumption prediction or a worst-caseconsumption prediction. As time goes on and history data 34 is recorded,usage patterns start repeating, and the neural network 430 consults thehistoric data 34 to learn how the settings of the heating elements120/122 and valves 130/132/134 need be adjusted based upon the historicdata 34. For example, the first Monday after installation, the neuralnetwork 430 has no historic data 34 and controls the heating elements120/122 and valves 130/132/134 to preheat the water in storage to acertain temperature based upon 40 unknown people. By measuring the flowrates, input water temperature and output water temperature, the neuralnetwork 430 determines that the water in the storage tank 140 does notneed to be heated as much as it was, so the next Monday, the water inthe storage tank 140 is heated 10% less and the flow rates, input watertemperature and output water temperature are again measured to determinehow well the high-efficiency hot water heating system is meeting thedemands of users.

The neural network 430 accepts data (as described above) and determinesactions (as described above), with the added benefit that the neuralnetwork learns. The neural networks find patterns in the data as well asfilter the data. For a home installation example, the neural network 430is initially taught that morning hot water demand starts at 7:00 AM andends at 8:00 AM. Over time, the neural network 430 finds that everyFriday morning between 6:00 and 6:15, there is a high demand for hotwater and, therefore, will make adjustments (e.g. learns) to bettersatisfy that early demand for hot water. The neural network 430 alsofilters its data to ignore irregular data. For example, when users inthe prior example are on vacation and there is no demand for hot wateron that day, the neural network 430 filters out the data from thevacation period.

In some high-efficiency hot water heater systems, the basic neuralnetwork software 430 is provided as a package from a provider of suchand is programmed based upon the range of inputs available to thehigh-efficiency hot water heater (e.g., schedule, ambient/weather,history, current data, external data, etc.) to control the availableheating elements 120/122 (see FIGS. 5 and 6) and/or valves 130/132/134.For some high-efficiency hot water heater, the neural network software430 is programmed from scratch.

In some high-efficiency hot water heaters, instead of using true neuralnetworks 430, heuristic algorithms or static logic is used in theprediction algorithms 30. A simple example in a dormitory in which allstudents leave for breakfast and class at the same time and there are nstudents registered for that dormitory, an exemplary heuristic algorithmis: if n is less than 30, preheat the water to t1 at time T1; if n isgreater than 30 and less than 60, preheat the water to t2 at time T2;and if n is greater than 60, preheat the water to t3 at time T3. Inthis, the schedule data 33 is used to determine when the students willbe using the hot water (e.g. before class/breakfast) and how manystudents are present. The more students present, the hotter the water inthe storage tank needs to be, therefore, heating starts earlier and endswhen the water reaches this higher temperature.

Equivalent elements can be substituted for the ones set forth above suchthat they perform in substantially the same manner in substantially thesame way for achieving substantially the same result.

It is believed that the system and method as described and many of itsattendant advantages will be understood by the foregoing description. Itis also believed that it will be apparent that various changes may bemade in the form, construction and arrangement of the components thereofwithout departing from the scope and spirit of the invention or withoutsacrificing all of its material advantages. The form herein beforedescribed being merely exemplary and explanatory embodiment thereof. Itis the intention of the following claims to encompass and include suchchanges.

What is claimed is:
 1. A high-efficiency water heating systemcomprising: at least one source of heat, the source of heat interfacedto a water supply such that the source of heat controllably transfersheat into water from the water supply, thereby producing heated water; aprocessor interfaced to the source of heat, the processor controllingthe operation of the source of heat; at least one source of data, thedata related to a consumption of the heated water from thehigh-efficiency water heating system; and software running on theprocessor, the software analyzing the data and the software calculatinga predicted demand for the heated water and the software controlling theoperation of the at least one source of heat responsive to the predicteddemand.
 2. The high-efficiency water heating system of claim 1, whereinthe at least one source of data is selected from the group consisting ofindoor ambient temperature, indoor ambient humidity, outdoor ambienttemperature, outdoor ambient humidity.
 3. The high-efficiency waterheating system of claim 1, further comprising a network interface, thenetwork interface operatively coupled to the processor and the networkinterface providing a connection between the processor and a network. 4.The high-efficiency water heating system of claim 3, wherein the atleast one source of data includes external data from an externalprovider, the external provider connected to the processor through thenetwork and the processor accessing the external data through thenetwork and the network interface.
 5. The high-efficiency water heatingsystem of claim 1, further comprising a water storage tank, at least oneof the at least one sources of heat being operatively coupled to thewater storage tank such that, when the at least one sources of heat thatis operatively coupled to the water storage tank is energized, heat istransferred to water within the storage tank to produce the heatedwater.
 6. The high-efficiency water heating system of claim 5, whereinat least one of the at least one sources of heat is an on-demand heatingelement.
 7. The high-efficiency water heating system of claim 6, furthercomprising at least one electrically controlled valve, each of the atleast one electrically controlled valve is operatively connected to theprocessor such that, under control of the processor, heated water issupplied from the water storage tank, the on-demand heating element(s),or a combination of both the water storage tank and the on-demandheating element(s).
 8. The high-efficiency water heating system of claim1, further comprising a flow sensor, the flow sensor operatively coupledto the processor, and the flow sensor providing to the processor ameasurement of current demand for the heated water.
 9. Thehigh-efficiency water heating system of claim 1, wherein the softwarerunning on the processor uses neural networks to calculate the predicteddemand.
 10. A method of providing heated water, the method comprising:(a) calculating a predicted demand for heated water over a period oftime; (b) prior to and during the period of time, signaling one or moresources of heat to energize based upon the predicted demand, the sourcesof heat interfaced to a water supply such that the sources of heatprovide heat to water to produce a quantity of the heated water; (c)measuring a rate of flow of the heated water during the period of time;and (d) feeding the rate of flow of the heated water back into the stepof calculating to improve the accuracy of the step of calculating; and(e) repeating steps a-e.
 11. The method of claim 10, wherein the step ofcalculating uses a neural network.
 12. The method of claim 10, whereinthe step of calculating takes into account ambient conditions.
 13. Themethod of claim 10, wherein the step of calculating takes into account aschedule of users of the heated water.
 14. The method of claim 10,wherein the step of calculating takes into account external data. 15.The method of claim 10, wherein the step of calculating takes intoaccount historic demand for heated water.
 16. A high-efficiency waterheating system comprising: at least source of heat operatively coupledto a water storage tank such that, when the at least one source of heatis energized, heat is transferred into water within the storage tank,thereby producing heated water; at least one on-demand heating deviceconnected to a supply of water such that, when the at least oneon-demand heating device is energized, heat is transferred into waterfrom a supply of water by the at least one on-demand heating device,thereby producing the heated water; a first electrically controlledvalve connected between the output of the storage tank and heated waterplumbing; a second electrically controlled valve connected between theoutput of the at least one on-demand heating device and the heated waterplumbing; a processor interfaced to the at least one source of heat andto the at least one on-demand heating device, the processor controllingthe operation of the at least one source of heat, the at least oneon-demand heating device, the first valve, and the second valve; a flowsensor, the flow sensor operatively coupled to the processor, and theflow sensor providing a measurement of current demand for the heatedwater; at least one source of data, the data related to a consumption ofthe heated water from the high-efficiency water heating system; andsoftware running on the processor, the software analyzing the data andthe measurement of current demand, the software calculating a predicteddemand for the heated water, and the software controlling the operationof the at least one source of heat, the at least one on-demand heatingdevice, the first valve, and the second valve, responsive to thepredicted demand.
 17. The high-efficiency water heating system of claim16, wherein the at least one source of data includes measurement dataselected from the group consisting of indoor ambient temperature, indoorambient humidity, outdoor ambient temperature, outdoor ambient humidity.18. The high-efficiency water heating system of claim 16, wherein thesoftware further records historical data that includes data from theflow sensor such that, in the future, the software uses the historicaldata to adjust the predicted demand based upon both the data and thehistorical data.
 19. The high-efficiency water heating system of claim16, wherein the software running on the processor uses neural networksto calculate the predicted demand.
 20. The high-efficiency water heatingsystem of claim 16, further comprising a plurality of sensors, thesensors interfaced to the processor, the sensors providing at least partof the data, wherein the sensors are selected from the group consistingof temperature sensors, humidity sensors, and ambient light sensors.